Forecast Combinations

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Forecast Combinations

Author Info

Timmermann, Allan G

Abstract

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the degree of correlation between forecast errors and the relative size of the individual models’ forecast error variances). Although the reasons for the success of simple combination schemes are poorly understood, we discuss several possibilities related to model misspecification, instability (non-stationarities) and estimation error in situations where the numbers of models is large relative to the available sample size. We discuss the role of combinations under asymmetric loss and consider combinations of point, interval and probability forecasts.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 5361.

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Date of creation: Nov 2005
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Handle: RePEc:cpr:ceprdp:5361

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

This paper has been announced in the following NEP Reports:

NEP-ALL-2005-12-09 (All new papers)
NEP-ECM-2005-12-09 (Econometrics)
NEP-ETS-2005-12-09 (Econometric Time Series)
NEP-FOR-2005-12-09 (Forecasting)

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Other versions:
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

Frank A.G. den Butter & Pieter W. Jansen, 2008. "Beating the Random Walk: a Performance Assessment of Long-term Interest Rate Forecasts," Tinbergen Institute Discussion Papers 08-102/3, Tinbergen Institute. [Downloadable!]
Emil Stavrev, 2006. "Measures of Underlying Inflation in the Euro Area: Assessment and Role for Informing Monetary Policy," IMF Working Papers 06/197, International Monetary Fund. [Downloadable!]
Barbara Rossi, 2005. "Are Exchange Rates Really Random Walks? Some Evidence Robust to Parameter Instability," International Finance 0503006, EconWPA. [Downloadable!]
Other versions:
- Barbara Rossi, 2005. "Are Exchange Rates Really Random Walks? Some Evidence Robust to Parameter Instability," Data 0503001, EconWPA. [Downloadable!]
- Rossi, Barbara, 2006. "Are Exchange Rates Really Random Walks? Some Evidence Robust To Parameter Instability," Macroeconomic Dynamics, Cambridge University Press, vol. 10(01), pages 20-38, February. [Downloadable!]
Massimo Guidolin & Carrie Fangzhou Na, 2007. "The economic and statistical value of forecast combinations under regime switching: an application to predictable U.S. returns," Working Papers 2006-059, Federal Reserve Bank of St. Louis. [Downloadable!]
Marcellino, Massimiliano, 2005. "Pooling-based data interpolation and backdating," CEPR Discussion Papers 5295, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
Other versions:
- Massimiliano Marcellino, 2005. "Pooling-based Data Interpolation and Backdating," Working Papers 299, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
- Massimiliano Marcellino, 2007. "Pooling-Based Data Interpolation and Backdating," Journal of Time Series Analysis, Blackwell Publishing, vol. 28(1), pages 53-71, 01. [Downloadable!] (restricted)
Richard A. Ashley. & Randall J. Verbrugge., 2006. "Mis-Specification and Frequency Dependence in a New Keynesian Phillips Curve," Working Papers e06-12, Virginia Polytechnic Institute and State University, Department of Economics. [Downloadable!]
Serena Ng & Matt Shum, 2007. "Detecting Information Pooling: Evidence from Earnings Forecasts after Brokerage Mergers," Advances in Economic Analysis & Policy, Berkeley Electronic Press, vol. 7(1), pages 1801-1801. [Downloadable!] (restricted)
Massimo Guidolin & Allan Timmerman, 2007. "Forecasts of U.S. short-term interest rates: a flexible forecast combination approach," Working Papers 2005-059, Federal Reserve Bank of St. Louis. [Downloadable!]
Other versions:
- Guidolin, Massimo & Timmermann, Allan G, 2007. "Forecasts of US Short-term Interest Rates: A Flexible Forecast Combination Approach," CEPR Discussion Papers 6188, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
- Guidolin, Massimo & Timmermann, Allan, 2009. "Forecasts of US short-term interest rates: A flexible forecast combination approach," Journal of Econometrics, Elsevier, vol. 150(2), pages 297-311, June. [Downloadable!] (restricted)
Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics. [Downloadable!]
Han Hong & Bruce Preston, 2008. "Bayesian Averaging, Prediction and Nonnested Model Selection," NBER Working Papers 14284, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)

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